Adaptive Auxiliary Particle Filter for Track-Before-Detect With Multiple Targets

Luis Ubeda-Medina; Angel F. Garcia-Fernandez; Jesus Grajal

doi:10.1109/TAES.2017.2691958

Adaptive Auxiliary Particle Filter for Track-Before-Detect With Multiple Targets

Luis Ubeda-Medina^*, Angel F. Garcia-Fernandez, Jesus Grajal

^*Corresponding author for this work

Department of Electrical Engineering and Automation

Research output: Contribution to journal › Article › Scientific › peer-review

31 Citations (Scopus)

Abstract

A novel particle filter for multiple target tracking with track-before-detect measurement models is proposed. Particle filters efficiently perform target tracking under nonlinear or non-Gaussian models. However, their application to multiple target tracking suffers from the curse of dimensionality. We introduce an efficient particle filter for multiple target tracking which deals with the curse of dimensionality better than previously developed methods. The proposed algorithm is tested and compared to other multiple target tracking particle filters.

Original language	English
Article number	7894277
Pages (from-to)	2317-2330
Number of pages	14
Journal	IEEE Transactions on Aerospace and Electronic Systems
Volume	53
Issue number	5
DOIs	https://doi.org/10.1109/TAES.2017.2691958
Publication status	Published - Oct 2017
MoE publication type	A1 Journal article-refereed

Keywords

MULTITARGET TRACKING
CONVERGENCE RESULT
BAYESIAN-APPROACH

Access to Document

10.1109/TAES.2017.2691958

Cite this

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title = "Adaptive Auxiliary Particle Filter for Track-Before-Detect With Multiple Targets",

abstract = "A novel particle filter for multiple target tracking with track-before-detect measurement models is proposed. Particle filters efficiently perform target tracking under nonlinear or non-Gaussian models. However, their application to multiple target tracking suffers from the curse of dimensionality. We introduce an efficient particle filter for multiple target tracking which deals with the curse of dimensionality better than previously developed methods. The proposed algorithm is tested and compared to other multiple target tracking particle filters.",

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